Lithography model generating method based on deep learning, and mask manufacturing method including the lithography model generating method

ABSTRACT

A reliable lithography model generating method reflecting a mask bias variation and a mask manufacturing method including the lithography model generating method are provided. The lithography model generating method includes preparing basic image data for learning, preparing transform image data that indicates a mask bias variation, generating a lithography model by performing deep learning by combining the basic image data and the transform image data, and verifying the lithography model.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based on and claims priority under 35 U.S.C. § 119to Korean Patent Application No. 10-2022-0077085, filed on Jun. 23,2022, in the Korean Intellectual Property Office, the disclosure ofwhich is incorporated by reference herein in its entirety.

BACKGROUND

Embodiments of the present disclosure relate to a mask manufacturingmethod, and more particularly, to a lithography model generating methodbased on deep learning, and a mask manufacturing method including thelithography model generating method.

In a semiconductor process, a photolithography process using a mask maybe performed to form a pattern on a semiconductor substrate, such as awafer. The mask may be called a pattern-transferred body having apattern shape of an opaque material, which is formed on a transparentbase layer material. To manufacture this mask, a layout of a requiredpattern is first designed, and then optical proximity correction (OPC)edlayout data obtained through OPC is transferred as mask tape-out (MTO)design data. Thereafter, based on the MTO design data, mask datapreparation (MDP) may be performed, and processes such as an exposureprocess may be performed on a substrate for the mask.

SUMMARY

Embodiments of the present disclosure provide a reliable lithographymodel generating method reflecting a mask bias variation and a maskmanufacturing method including the lithography model generating method.

In addition, problems to be solved by and solutions of embodiments ofthe present disclosure are not limited to the problems and solutionsdescribed above, and other problems and solutions may be clearlyunderstood to those of ordinary skill in the art from the descriptionbelow.

According to embodiments of the present disclosure, a lithography modelgenerating method based on deep learning is provided. The lithographymodel generating method includes: preparing basic image data forlearning; preparing transform image data that indicates a mask biasvariation; generating a lithography model by performing deep learning bycombining the basic image data and the transform image data; andverifying the lithography model.

According to embodiments of the present disclosure, a lithography modelgenerating method based on deep learning is provided. The lithographymodel generating method includes: preparing basic image data forlearning; preparing transform image data that indicates a mask biasvariation; generating a lithography model by performing deep learning bycombining the basic image data and the transform image data; verifyingthe lithography model; and adjusting a recipe with respect to thelithography model, wherein the transform image data is generated throughan image augmentation method from the basic image data such that thetransform image data indicates a process variation.

According to embodiments of the present disclosure, a mask manufacturingmethod is provided. The mask manufacturing method includes: generating alithography model based on deep learning; generating an opticalproximity correction (OPC)ed layout by performing OPC on a mask layoutby using an OPC model obtained based on the lithography model;transferring the OPCed layout as mask tape-out (MTO) design data;preparing mask data based on the MTO design data; and exposing asubstrate for a mask based on the mask data. The generating of thelithography model includes: preparing basic image data for learning;preparing transform image data that indicates a mask bias variation;generating the lithography model by performing deep learning bycombining the basic image data and the transform image data; verifyingthe lithography model; and adjusting a recipe with respect to thelithography model, and wherein the lithography model includes an opticalproximity correction (OPC) model or a process proximity correction (PPC)model.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments of the present disclosure will be more clearly understoodfrom the following detailed description taken in conjunction with theaccompanying drawings in which:

FIG. 1 is a flowchart schematically illustrating a process of alithography model generating method based on deep learning according toan embodiment;

FIG. 2A is a first graph illustrating a critical dimension (CD) changewith respect to a process variation;

FIG. 2B is a second graph illustrating a CD change with respect to aprocess variation;

FIG. 3 is a conceptual diagram illustrating a problem of a lithographymodel generating method based on deep learning of a comparative example;

FIG. 4 is a conceptual diagram illustrating a method of generatingtransform image data through a data augmentation method in thelithography model generating method of FIG. 1 ;

FIG. 5 is a conceptual diagram illustrating the lithography modelgenerating method of FIG. 1 through deep convolutional generativeadversarial networks (DCGAN);

FIG. 6 is a block diagram illustrating a structure of DCGAN used in thelithography model generating method of FIG. 1 ;

FIG. 7A is a conceptual diagram illustrating a method of reflecting amask bias variation in a lithography model generating method of thecomparative example;

FIG. 7B is a conceptual diagram illustrating a method of reflecting amask bias variation in a lithography model generating method of thelithography model generating method of an embodiment of the presentdisclosure;

FIG. 8A is a graph illustrating an error root mean square (RMS) and anerror range with respect to a metal contact of a DRAM product in alithography model generating method that does not reflect the mask biasvariation;

FIG. 8B is a graph illustrating an RMS and an error range with respectto a metal contact of a DRAM product in the lithography model generatingmethod of an embodiment of the present disclosure;

FIG. 9 is a flowchart schematically illustrating a mask manufacturingmethod including a lithography model generating method according to anembodiment.

DETAILED DESCRIPTION

Hereinafter, non-limiting example embodiments of the present disclosureare described in detail with reference to the accompanying drawings.Like reference numerals in the drawings denote like elements, and theirrepetitive descriptions are omitted.

FIG. 1 is a flowchart schematically illustrating a process of alithography model generating method based on deep learning according toan embodiment, FIGS. 2A and 2B are graphs illustrating a criticaldimension (CD) change with respect to a process variation, and FIG. 3 isa conceptual diagram illustrating a problem of a lithography modelgenerating method based on deep learning of a comparative example.

Referring to FIGS. 1 to 3 , in the lithography model generating methodbased on deep learning of an embodiment of the present disclosure(hereinafter simply referred to as “lithography model generatingmethod”), first, basic image data for learning is prepared (operationS110). Here, the basic image data may be data of a pattern image of asample obtained through a measurement device. For example, in thelithography model generating method of the present embodiment, the basicimage data may include data of an after develop inspection (ADI) contourimage and an after clean inspection (ACI) contour image of a sample.Also, according to an embodiment, the basic image data may include dataof a mask layout image.

The sample may be a semiconductor device used in learning, and a patternof the sample may be formed by transferring a pattern of a mask onto thesample through an exposure process. Accordingly, first, a layout for thepattern of the mask corresponding to the pattern of the sample, that is,the mask layout, may be designed. For reference, in general, the shapeof the pattern of the sample may be different from the shape of thepattern of the mask due to the nature of the exposure process. Inaddition, because the pattern on the mask is reduced-projected andtransferred onto the substrate, the pattern of the mask may have agreater size than the pattern of the sample.

When the pattern of the mask is transferred onto the sample, a photoprocess and an etching process may be performed. In general, the photoprocess may refer to a process of forming a photoresist (PR) pattern ona sample through an exposure process and a development process. Also,the etching process may refer to a process of forming a pattern on asample by using a PR pattern as an etch mask.

In the photo process, optical proximity correction (OPC) may beperformed. As the pattern is refined, an optical proximity effect (OPE)occurs due to the influence between neighboring patterns in the exposureprocess, and OPC may include a method of correcting a mask layout tosuppress the OPE. OPC may include a process of generating an OPC model,and a process of generating an OPCed layout through simulation using theOPC model. Accordingly, the photo process may include a process ofgenerating an OPCed layout through OPC, a process of manufacturing amask with the OPCed layout, and a process of forming a PR pattern on asample through an exposure process using the mask and a developingprocess. Meanwhile, in the etching process, to compensate for an etchbias, process proximity correction (PPC) may be performed.

In the lithography model generating method of the present embodiment, alithography model may be an OPC model or a PPC model generated throughlearning. Also, input data used for learning and output datacorresponding thereto may vary depending on a process. For example, whenthe process is a photo process using a mask, the input data may be dataof a mask layout image, and the output data may be data of an ADIcontour image of a sample or data of an OPCed layout image. In addition,when the process is an etching process using a PR pattern, the inputdata may be data of an ADI contour image of the sample, and the outputdata may be data of an ACI contour image of the sample.

After preparing the basic image data, transform image data reflecting amask bias variation is prepared (operation S130). The mask biasvariation may include a process variation such as a dose or focusvariation of an exposure process, or an anchor pattern variation. Here,the anchor pattern may include a pattern representing patterns in themask with respect to a shape and a position. As shown in the graphs ofFIGS. 2A and 2B, a CD increases as the dose increases in the exposureprocess. In addition, the CD decreases as a focus moves away from anormal focus FO in the exposure process. Therefore, in order to be areliable lithography model, such a mask bias variation may need to bereflected in the lithography model.

The transform image data may include image data corresponding to themask bias variation, that is, the process change. In the lithographymodel generating method of the present embodiment, instead of preparingactual image data corresponding to each process variation, image datatransformed from the basic image data through a data augmentation methodis prepared as the transform image data. A process of obtaining thetransform image data from the basic image data through the dataaugmentation method is described in more detail with reference to FIG. 4.

After preparing the transform image data, deep learning is performed bycombining the basic image data and the transform image data and alithography model is calculated (operation S150). Here, deep learningmay be performed using deep convolutional generative adversarialnetworks (DCGAN). A structure of the DCGAN is described in more detailwith reference to FIG. 6 .

In the lithography model generating method of the present embodiment,deep learning may be performed by combining the basic image data and thetransform image data. In addition, deep learning may be performed whilechanging a combination for each iteration by applying a weight to thebasic image data and the transform image data. Here, the basic imagedata and the transform image data may be appropriately combined and usedfor deep learning so as to minimize a learning time while generating anoptimized lithography model.

For example, when at least 100 pieces of image data are used to generatean optimal lithography model, in the lithography model generating methodof the present embodiment, deep learning may be performed by combiningthe image data and the transform image data such that 100 pieces ofimage data are obtained. In addition, deep learning may be performedwhile changing the combination for each iteration. For example, in afirst iteration, 80 pieces of basic image data, 10 first transform imagedata, and 10 pieces of second transform image data may be used for deeplearning, and in a second iteration, 70 pieces of basic image data, 20pieces of first transform image data, and 10 pieces of second transformimage data may be used for deep learning. Here, the first transformimage data and the second transform image data may correspond to imagedata having different variation values. The combination of the basicimage data and the transform image data, and deep learning accordingthereto are described in more detail with reference to FIGS. 5 to 8B.

On the other hand, a weight is a factor that gives the influence orresponsiveness of image data with regard to learning. For example, inthe lithography model generating method of the present embodiment, aweight of 0.7 may be allocated to the basic image data having a greatinfluence, and weights of 0.2 and 0.1 may be respectively allocated tothe first transform image data and the second transform image datahaving a relatively little influence. However, weight allocation valuesare not limited thereto. On the other hand, once the weights areassigned, the weights may be maintained in all iterations. However,according to an embodiment, the weights may be changed for eachiteration.

In the lithography model generating method of the present embodiment,the lithography model is a model generated in learning through theDCGAN, and may be an OPC model or a PPC model. In other words, accordingto the input data and the output data, the lithography model may be theOPC model or the PPC model. For example, when the lithography model isthe OPC model, the input data may be data of a mask layout image, andthe output data may be data of an ADI contour image of a sample, or dataof an OPCed mask layout image. In addition, when the lithography modelis the PPC model, the input may be data of the ADI contour image of thesample, and the output may be data of the ACI contour image of thesample.

After generating the lithography model, verification of the lithographymodel is performed (operation S170). When verification is passed (Pass),the process proceeds to operation S190 of adjusting a recipe, and whenverification is not passed (Fail), the process proceeds to operationS150 of generating the lithography model.

Verification of the lithography model may be generally performed with anerror root mean square (RMS). For example, when the error RMS is greaterthan a set reference value by comparing image data output through thelithography model with reference image data, verification may not bepassed, and when the error RMS is less than or equal to the referencevalue, verification may be passed. Here, the error RMS may be, forexample, the error RMS with respect to the CD. According to anembodiment, verification may be performed using an edge placement error(EPE).

When verification of the lithography model is passed (Pass), the recipeof the lithography model is adjusted (operation S190). When thelithography model is the OPC model, a recipe of the OPC model may beadjusted, and when the lithography model is the PPC model, a recipe ofthe PPC model may be adjusted. In other words, when the lithographymodel is the OPC model, some of recipes constituting the existing OPCmodel may be changed based on the generated lithography model. Inaddition, when the lithography model is the PPC model, some of recipesconstituting the existing PPC model may be changed based on thegenerated lithography model. Through such an adjustment of the recipe, afinal lithographic model, that is, the OPC model and/or the PPC model,may be completed.

In the lithography model generating method of the present embodiment,the transform image data reflecting the mask bias variation may beautomatically generated from the basic image data through the dataaugmentation method. In addition, the learning time may be minimized byperforming deep learning while changing the combination for eachiteration by applying the weights to the basic image data and thetransform image data. Furthermore, a reliable lithography model thatresponds to the mask bias variation may be generated, by adding thetransform image data reflecting the mask bias variation to a deeplearning system. As a result, the lithography model generating method ofthe present embodiment may make it possible to manufacture a reliablemask capable of accurately forming a required pattern on a semiconductordevice based on the reliable lithography model.

In a more general description of the mask, in order to pattern asemiconductor device in a semiconductor process, it may be necessary tomanufacture the mask for a lithography process. Mask manufacture startsby generating a lithography model, generates an OPCed layout imagethrough simulation such as OPC/PPC, and undergoes a mask tape-out (MTO)process to manufacture the final mask by a mask manufacturing team. Amask manufacturing method is described in more detail with reference toFIG. 9 .

The quality of the mask affects patterning matching of the semiconductordevice, and, in particular, the accuracy of the lithography model may bethe most important factor in the quality of the mask. Accordingly, inorder to improve the accuracy of the lithography model, artificialintelligence (AI) technology may be applied to OPC. For example, a largenumber of images may be trained through deep learning to generate thelithography model. On the other hand, the lithography model may need tohave predictive power for various process variations. Among the processvariations, the mask bias variation is typical. As described above, themask bias variation may include process conditions with respect tovariations in dose and focus in the exposure process, a variation in ananchor pattern, etc.

In the case of general lithography model based on deep learning, whilean improved result is shown in terms of accuracy, there is a problem oflow reliability in terms of predictability of mask bias variations in acomparative embodiment. In the lithography model based on deep learning,in order to improve the predictability of process variations,information about process variations may be added to a deep learningprocess. However, in deep learning, when all data of process variationsis transformed into image data and input to deep learning, it mayencounter a time limit in learning due to a huge increase in input data.

Specifically, in a lithography model generating method based on deeplearning according to a comparative embodiment, in order to improve theresponsiveness to mask bias variations, a method of generating a basiclithography model with respect to the basic image data, adding imagedata of the mask bias variation (hereinafter, referred to as a “maskbias split”) again, and compensating for the lithography model isperformed. However, such a method is very disadvantageous in terms ofefficiency of the learning time because the learning time is multipliedby a multiple of the mask bias split. For example, referring to FIG. 3 ,when one sheet corresponds to the basic image data in input data Inputand the remaining two sheets correspond to the mask bias split, in orderto reflect the mask bias variation, the input data Input may beincreased to three times the base image data. Accordingly, three timesthe learning time may be required to calculate the appropriate outputdata Output and the lithography model, even when the arithmeticcalculation is simply performed. In addition, considering the time forpreparing the mask bias split, it may take more time to calculate thelithography model.

However, in the lithography model generating method of the presentembodiment, the transform image data in response to the mask biasvariation may be automatically generated based on the data augmentationmethod. In addition, by performing deep learning by applying weightswhile dynamically and randomly combining the basic image data and thetransform image data for each iteration, the learning time may beminimized, and the reliable lithography model that reflects the errordue to the mask bias variation may be generated.

FIG. 4 is a conceptual diagram illustrating a method of generatingtransform image data through a data augmentation method in thelithography model generating method of FIG. 1 .

Referring to FIG. 4 , the data augmentation method refers to a method ofgenerating a new image by applying an appropriate modification to anoriginal image. For example, in the lithography generation method of thepresent embodiment, the transform image data may be generated byapplying an appropriate modification to basic image data through thedata augmentation method.

As a specific example, the data augmentation method may generate a newimage by slightly moving the original image up, down, left and right,slightly rotating the original image, slightly tilting the originalimage, slightly enlarging or reducing the original image, etc. Inaddition, the data augmentation method may significantly increase thenumber of pieces of image data, by generating a new image by combiningat least two transformations of slight translation, slight rotation,slight tilting, slight enlargement, and slight reduction of the originalimage.

FIG. 4 illustrates the method of generating the new image by rotation,symmetry, or combination of rotation and symmetry of the original image.For example, the new image may be generated by rotating the originalimage by 90 degrees, 180 degrees, or 270 degrees. In addition, the newimage may be generated through X-axis symmetry or Y-axis symmetry of theoriginal image. Furthermore, the new image may be generated by combiningany one of 90 degree rotation, 180 degree rotation, and 270 degreerotation of the original image, and any one of X-axis symmetry andY-axis symmetry of the original image.

In FIG. 4 , the concept of generating the new image through the dataaugmentation method has been described with the concept of rotation atan interval of 90 degrees and symmetry of the X and Y axes, butgeneration of the new image through the data augmentation method is notlimited thereto. For example, the generation of the new image throughthe data augmentation method may include generation of the new image byfine rotation, fine horizontal/vertical translation, fineenlargement/reduction, etc. For example, in the lithography generatingmethod of the present embodiment, the transform image data may begenerated from the basic image data through fine horizontal/verticaltranslation or fine enlargement/reduction by the data augmentationmethod.

Meanwhile, the new image may be automatically generated from theoriginal image through the data augmentation method. For example, in atool (e.g., a computer) with respect to the data augmentation method,when a user selects at least one of rotation, symmetry, translation,enlargement, or reduction, sets parameter values to be applied, and theninputs the original image to the tool, the new image is automaticallygenerated. As a specific example, in the tool with respect to the dataaugmentation method, when the user selects enlargement and reduction,sets a parameter value of 1 nm, and then inputs a basic image to thetool, a new image corresponding to ±1 nm with respect to the basic imagemay be automatically generated.

FIG. 5 is a conceptual diagram illustrating the lithography modelgenerating method of FIG. 1 through DCGAN.

Referring to FIG. 5 , in the lithography model generating method of thepresent embodiment, a lithography model may be generated, by performingdeep leaning using a combination of basic image data Original-i andtransform image data Biased-i as the input data Input, and basic imagedata Original-o and transform image data Biased-o respectivelycorresponding thereto as the output data Output. As described above, anappropriate number of the basic image data Original-i and the transformimage data Biased-i may be used as the input data Input through acombination, and the combination may be changed for each iteration. Inaddition, weights with respect to the basic image data Original-i andthe transform image data Biased-i may be fixedly set or may be setdifferently for each iteration.

As described above, in the lithography model generating method of thepresent embodiment, the transform image data reflecting a mask biasvariation may be generated through a data augmentation method, and deeplearning may be performed by dynamically combining the basic image dataand the transform image data for each iteration and applying appropriateweights. Accordingly, a reliable lithography model that recognizes andresponds to the mask bias variation may be generated.

FIG. 6 is a block diagram illustrating a structure of DCGAN used in thelithography model generating method of FIG. 1 .

Referring to FIG. 6 , before describing the structure of DCGAN, brieflydescribing GAN, GAN is a generative algorithm based on deep learning,and may include two sub-models. That is, the GAN may include a generatormodel and a discriminator model. The generator model may correspond to alithography model in the lithography model generating method of thepresent embodiment. The generator model generates new examples, and thediscriminator model determines whether a generated example is actualdata or fake data generated by the generator model.

For example, with respect to the lithography model generating method ofthe present embodiment, the generator model may transform an input imageto generate an output image corresponding to an image after OPC or PPC.For example, in an OPC process, an input image provided to the generatormodel may be a mask layout image, and an output image from the generatormodel may be an ADI contour image or an OPCed layout image. In addition,in a PPC process, an input image provided to the generator model may bean ACI image, and an output image from the generator model may be an ADIcontour image.

An output image generated by the generator model and a reference imagemay be input to the discriminator model. Here, the reference image maycorrespond to a final image, which an output image is supposed to reach.For example, when an output image is an ADI contour image, the referenceimage may be a target PR pattern image on an actual substrate. Inaddition, when an output image is an ACI contour image, the referenceimage may be a target pattern image on an actual substrate. Thediscriminator model compares an output image with the reference imageand determines whether the output image is an actual image or a fakeimage generated by the generator model. In other words, thediscriminator model may determine that an output image is an actualimage when the output image is substantially the same as the referenceimage, and determine that the output image is a fake image when theoutput image differs from the reference image.

Specifically, for example, when a mask layout image is input to thegenerator model as an input image, the generator model generates anoutput image, which is an ADI contour image. Thereafter, the outputimage and the reference image are input to the discriminator model.Here, the reference image may correspond to a target PR pattern image onan actual substrate. Thereafter, the discriminator model determineswhether the output image is the same as the reference image. Forexample, the discriminator model determines whether an ADI contour imagegenerated by the generator model is the same as a target PR patternimage on an actual substrate. Thereafter, according to a result of thedetermination, the generator model and the discriminator model arecontinuously updated. When the discriminator model cannot discriminatethe output image OPI from the reference image RI any more according torepeating this procedure described above, deep learning ends, and thegenerator model at this time point may be adopted as a final lithographymodel. When deep learning ends, the discriminator model is discarded.

In the lithography model generating method of the present embodiment, inorder for the generator model of the GAN, i.e., a lithography model, togenerate a relatively accurate image, features may be accuratelyextracted from input images. To extract the features, a convolutionprocess, as shown in FIG. 6 , may be included. Accordingly, in thelithography model generating method of the present embodiment, the GANmay be the DCGAN. The convolution process is performed using aconvolution filter and may include a down-sampling process and anup-sampling process. In addition, for relatively accurate learning,residual learning may be included between the down-sampling process andthe up-sampling process. Through the residual learning, an opticaleffect of a peripheral region may be reflected.

In the lithography model generating method of the present embodiment, aninput image may be down-sampled multiple times to obtain, for example, aone-time down-sampled image down1, a two times down-sampled image down2, and a three times down-sampled image down3, and up-sampled multipletimes to obtain a one time up-sampled image up1, a two times up-sampledimage up2, a three times up-sampled image up3, and a four timesup-sampled image up4, such that there are different scales provided(e.g., scale1, scale2, scale3, scale4). According to embodiments, thethree times down-sampled image down3 may be used for residual learning,then up-sampled, concatenated with a previous image having undergoneresidual learning, and up-sampled. The number of times down-sampling isnot limited to three. For example, according to an embodiment, residuallearning may be performed with each image having undergone down-samplingonce or twice, or down-sampling four times or more.

In the lithography model generating method of the present embodiment,the DCGAN may include a plurality of down-sample layers to have astructure reflecting a pixel correlation up to a far distance. Everytime an input image passes through a down-sample layer, the input imagemay be reduced to a half size at an output layer. However, because areduced image still implies pattern information corresponding to thesame width as the width of the input image, information represented byone pixel may correspond to two times (or four times in an area concept)of that of the input image. As a result, even though kernels of the samesize are used, a kernel applied to an image having passed through agreater number of down-sample layers may represent a pixel correlationof a wider region.

In addition, with respect to residual learning, although a residualblock first structure is used in FIG. 6 , a residual block laststructure may be used instead. Residual learning is performed afterdown-sampling in the residual block first structure, whereasdown-sampling may be performed after residual learning in the residualblock last structure. Although image synthesis may be performed using aconcatenation layer structure, the image synthesis may be performedusing a sum-fusion layer structure. The concatenation layer structurehas a twice larger structure in a channel direction, and thus, a kernelis also larger, and a greater number of parameters are included. On thecontrary, the sum-fusion layer structure is generated through anelementwise sum, and thus, an output result of a similar performance maybe obtained while maintaining a small-sized kernel.

FIGS. 7A and 7B are conceptual diagrams respectively illustrating amethod of reflecting a mask bias variation in a lithography modelgenerating method of the comparative example and the lithography modelgenerating method of the present embodiment.

Referring to FIG. 7A, 0 is a sheet with respect to basic image data of aCD size, −1 is a sheet with respect to first additional image data of aCD size as small as 1 nm, and 1 is a sheet with respect to secondadditional image data of a CD size as large as 1 nm. In the lithographymodel generating method of the comparative example, deep learning isperformed by preparing all image data with respect to a CD size change.In other words, in order to reflect the mask bias variation related tothe CD size change, a lithography model is generated by preparing thebasic image data 0, the first additional image data −1, and the secondadditional image data 1 as the input data Input and performing deeplearning.

Meanwhile, as may be seen from FIG. 7A, in response to each of the basicimage data 0, the first additional image data −1, and the secondadditional image data 1 of the input data Input, the output data Outputof each of the basic image data 0, the first additional image data −1,and the second additional image data 1 is obtained. For reference, theCD size change may be due to a process variation such as a dosevariation and a focus variation as mentioned above.

As described above, in the case of the lithography model generatingmethod of the comparative example, in order to reflect the mask biasvariation, deep learning is performed by preparing all additional imagedata related to the process variation. Therefore, in the lithographymodel generating method of the comparative example, a deep learning timemay increase by an amount of additional image data. For example, whenthe same number of pieces of additional image data as that of the basicimage data is prepared per parameter of the process variation, in thecase of the CD size change, the input data Input may increase threetimes, and accordingly, the deep learning time may also increase threetimes.

Referring to FIG. 7B, in the case of the lithography model generatingmethod of the present embodiment, the first transform image data −1corresponding to a CD size decrease and the second transform image data1 corresponding to a CD size increase may be automatically generatedthrough a data augmentation method without separately preparing thefirst transform image data −1 and the second transform image data 1. Inaddition, in deep learning, the basic image data 0, the first transformimage data −1, and the second transform image data 1 are not all usedbut may be combined with each other and used for deep learning by thenumber of pieces of basic image data 0.

For example, when the number of the basic image data 0 is 100, 100pieces of first transform image data −1 and 100 pieces of secondtransform image data 1 each may be generated by the data augmentationmethod. However, in deep learning, all of 300 pieces of data are notused as the input data Input, but 100 pieces of data by a combination of80 pieces of basic image data 0, 10 pieces of first transform image data−1, and 10 pieces of second transform image data 1 may be used as theinput data Input. On the other hand, in deep learning, a weight isapplied to each piece of data, and the combination may be changed foreach iteration. In other words, a weight of 0.8 may be allocated to thebasic image data 0, a weight of 0.1 to the first transform image data−1, and a weight of 0.1 to the second transform image data 1. Also, thecombination may be changed, such as (80, 10, 10) in a first iteration,(70, 20, 10) in a second iteration, and (85, 5, 10) in a thirditeration.

As a result, in the lithography model generating method of the presentembodiment, deep learning is performed with the same number of pieces ofdata as the number of pieces of basic image data, and thus, deeplearning may be performed with substantially the same time as the timetaken to perform deep learning with the basic image data. In addition,the lithography model generating method of the present embodiment maygenerate a reliable lithography model that actively responds to the maskbias variation, by adding the transform image data reflecting the maskbias variation to deep learning.

TABLE 1 below shows an amount of data of a learning image and averification image used for deep learning in the lithography generatingmethod of the comparative example and the lithography model generatingmethod of the present embodiment with respect to a metal contact of aDRAM product. For reference, a lithography model generated by thelithography model generating method of each of the comparative exampleand the present embodiment may be a PPC model.

In TABLE 1, “Ref” means a lithography model generating method that doesnot reflect the mask bias variation, “Com.” means the lithography modelgenerating method of the comparative example, and “Emb.” means thelithography model generating method of the present embodiment. Inaddition, the “learning image” means the number of pieces of image dataused for deep learning, and the “verification image” means the number ofpieces of image data used for verification of the lithography model.Meanwhile, in the case of Com., as described above with reference toFIG. 7A, additional image data in which the CD size is changed by ±1 nmis used for deep learning.

TABLE 1 learning image verification image Ref. 1,735 442 Com. 5,205 (±1nm) 442 Emb. 1,735 442

As may be seen from TABLE 1, Com. uses more image data three times fordeep learning than Ref, whereas Emb. may use the same number of piecesof image data as that of Ref for deep learning. Meanwhile, theverification image is image data used for verification after generatingthe lithography model, and may all be set to the same number. Forexample, in TABLE 1, all of verification images are set to 442.

TABLE 2 below shows the effect on the lithography model in relation toTABLE 1 above. The meaning of “Ref.,” “Com.,” and “Emb.” may be the sameas in TABLE 1.

TABLE 2 Cal. errRMS Val. errRMS learning time note Ref. 0.24 0.307 27 hCom. 0.235 0.212 82 h Emb. 0.238 0.216 27 h three times reduction inlearning time

In [TABLE 2], “errRMS” may indicate an error RMS value with respect to aCD, “Cal.” may indicate image data adjusted through deep learning, and“Val.” may indicate verification image data. It may be seen that becauseCal. is not a result by a final lithography model, error RMSs of Ref.,Com., and Emb. show similar results. On the other hand, it may be seenthat Val. is a result by the final lithography model, Ref that does notconsider the mask bias variation has a relatively great error RMS, andCom. and Emb. have an almost similar error RMS. On the other hand, withregard to the learning time, it may be seen that Ref and Emb. have thesubstantially the same learning time, and Com. has three times or morelearning time. In conclusion, in the case of the lithography modelgenerating method of the present embodiment, the learning time may bereduced by a multiple of the data increased in Com. while having thesame effect as that of Com.

FIGS. 8A and 8B are graphs illustrating an error RMS and an error rangewith respect to a metal contact of a DRAM product in a lithography modelgenerating method that does not reflect the mask bias variation and thelithography model generating method of the present embodiment. In thegraphs of FIGS. 8A and 8B, a part 80-100 may correspond to a CD of ashort axis of the metal contact, and a part 220-240 may correspond to aCD of a long axis of the metal contact.

Referring to FIG. 8A, in the lithography model generating method of thecomparative example that does not reflect the mask bias variation, theerror RMS with respect to a verification image is about 0.307, and theerror range is about 4.3 nm. On the other hand, Referring to FIG. 8B, inthe lithography model generating method of the present embodiment, theerror RMS with respect to the verification image is about 0.246, and theerror range is about 3.0 nm. As a result, it may be confirmed that areliable lithography model reflecting the mask bias variation may begenerated through the lithography model generating method of the presentembodiment.

FIG. 9 is a flowchart schematically illustrating a mask manufacturingmethod including a lithography model generating method according to anembodiment. The mask manufacturing method is described with reference toFIG. 1 , and descriptions already given with reference to FIG. 1 arebriefly provided or omitted.

Referring to FIG. 9 , in the mask manufacturing method including thelithography model generating method (hereinafter, simply “maskmanufacturing method”) of the present embodiment, first, a lithographymodel is generated (operation S210). Operation S210 of generating thelithography model may be substantially the same as the lithography modelgenerating method of FIG. 1 . For example, Operation S210 of generatingthe lithography may include a basic image data preparation operation, atransform image data preparation operation, a lithography modelcalculation operation by performing deep learning, a lithography modelverification operation, and a recipe adjustment operation. In addition,in the transform image data preparation operation, the transform imagedata is obtained through the data augmentation method, and in thelithography model calculation operation, deep learning is performed bycombining the basic image data and the transform image data, whilechanging a combination for each iteration by applying weights.

After generating the lithography model, an OPCed layout image withrespect to a mask layout image is generated by performing OPC (operationS230). Here, the OPC may indicate general OPC. The general OPC mayinclude a method of adding sub-lithographic features, called serifs, orSRAFs, such as scattering bars, onto a corner of a pattern in additionto a shape change in a layout of a pattern.

Performing OPC may include first preparing basic data for the OPC,generating an optical OPC model, generating an OPC model with respect toa PR, etc. A combination of the optical OPC model and the OPC model withrespect to the PR is generally called an OPC model. Meanwhile, thelithography model may be used prior to generation of the OPC model. Forexample, the lithography model may be used for recipe adjustment of theOPC model. After the OPC model is generated, an OPCed layout image maybe generated by performing simulation using the OPC model on the masklayout image.

Thereafter, the OPCed layout image is transferred to a maskmanufacturing team as MTO design data (operation S250). In general, MTOmay indicate transferring final mask data obtained by an OPC method tothe mask manufacturing team to request mask manufacturing. Therefore,the MTO design data may be substantially the same as data of the OPCedlayout image obtained through OPC. The MTO design data may have agraphic data format used in electronic design automation (EDA) software,etc. For example, the MTO design data may have a data format, such asgraphic data system II (GDS2) or open artwork system interchangestandard (OASIS).

On the other hand, before transferring the OPCed layout image as the MTOdesign data to the mask manufacturing team, an optical rule check (ORC)on the OPCed layout image may be performed. The ORC may include, forexample, RMS with respect to a CD error, EPE, a pinch error, a bridgeerror, etc. However, items inspected by the ORC are not limited to theabove items. The OPCed layout image that has passed the ORC may betransferred to the mask manufacturing team as the MTO design data.

Thereafter, mask data preparation (MDP) is performed (operation S270).The MDP may include, for example, i) format transform called fracturing,ii) augmentation of a barcode for mechanical reading, a standard maskpattern for inspection, a job deck, etc., and iii) validation in anautomatic and manual manners. Herein, the job deck may indicategenerating a text file related to arrangement information of multi-maskfiles, a reference dose, and a series of instructions related to anexposure speed and scheme etc.

In addition, the format transform, i.e., fracturing, may indicate aprocess of fracturing the MTO design data for each region to change in aformat for an electron beam writer. Fracturing may include a dataoperation of, for example, scaling, data sizing, data rotation, patternreflection, color inversion, etc. In transforming through fracturing,data related to many systematic errors, which may occur somewhere duringtransferring from design data to an image on a wafer, may be corrected.The data correction of the systematic errors is called mask processcorrection (MPC) and may include, for example, line width adjustment,called CD adjustment, a work for increasing pattern arrangementprecision, etc. Therefore, fracturing may contribute to improvement ofthe quality of a final mask, and may also be a process performed inadvance to correct a mask process. Herein, the systematic errors may becaused by distortion occurring in an exposure process, a maskdevelopment and etching process, a wafer imaging process, etc.

The MDP may include MPC. The MPC indicates a process of correcting anerror, i.e., a systematic error, occurring during an exposure process,as described above. Herein, the exposure process may be a conceptgenerally including electron beam writing, development, etching, baking,etc. In addition, data processing may be performed before the exposureprocess. The data processing is a kind of pre-processing on mask dataand may include grammar check on the mask data, exposure timeprediction, etc.

After performing the MDP, a substrate for a mask is exposed to lightbased on the mask data (operation S290). Here, the exposure mayindicate, for example, electron beam writing. Here, the electron beamwriting may be performed by, for example, a gray writing scheme using amulti-beam mask writer (MBMW). Alternatively, the electron beam writingmay be performed using a variable shape beam (VSB) writer.

In addition, after performing the MDP, an operation of transforming themask data into pixel data before the exposure process. The pixel data isdata directly used for actual exposure and may include data about ashape to be exposed to light and data about a dose allocated to each ofthe pieces of data about the shape. Here, the data about the shape maybe bit-map data transformed from shape data, which is vector data,through rasterization etc.

After the exposure process, a series of processes are performed tocomplete a mask. The series of processes may include, for example, adevelopment process, an etching process, a cleaning process, etc. Inaddition, the series of processes for mask manufacturing may include ameasurement process and a defect inspection and repair process. Inaddition, a pellicle coating process may be included. Here, the pelliclecoating process may indicate a process of attaching pellicles to protectthe surface of a mask from possible contamination during mask deliveryand a mask available life span after confirming through final cleaningand inspection that there are no contamination particles and chemicalstains.

The mask manufacturing method of the present embodiment may include alithography model generating method based on deep learning.Specifically, the mask manufacturing method of the present embodimentmay generate a lithography model through a basic image data preparationoperation, a transform image data preparation operation, a lithographymodel calculation operation by performing deep learning, a lithographymodel verification operation, and a recipe adjustment operation.Accordingly, the mask manufacturing method of the present embodiment mayaccurately generate an OPCed layout image based on the lithography modelgenerating method based on deep learning. As a result, the maskmanufacturing method of the present embodiment may manufacture areliable mask capable of accurately forming a required pattern on asemiconductor device based on an accurate OPCed layout image.

According to embodiments, at least one processor and memory storingcomputer instructions may be provided. According to embodiments, thecomputer instructions, when executed by the at least one processor, mayperform any number of functions described in the present disclosureincluding, for example, the operations of the methods described withreference to FIGS. 1 and 9 .

While non-limiting example embodiments of the present disclosure havebeen particularly shown and described with reference to the drawings, itwill be understood that various changes in form and details may be madeto the example embodiments without departing from the spirit and scopeof the present disclosure.

What is claimed is:
 1. A lithography model generating method based ondeep learning, the lithography model generating method comprising:preparing basic image data for learning; preparing transform image datathat indicates a mask bias variation; generating a lithography model byperforming deep learning by combining the basic image data and thetransform image data; and verifying the lithography model.
 2. Thelithography model generating method of claim 1, wherein the transformimage data is image data transformed from the basic image data such thatthe transform image data indicates a process variation.
 3. Thelithography model generating method of claim 2, wherein the transformimage data is generated from the basic image data through an imageaugmentation method.
 4. The lithography model generating method of claim3, wherein the image augmentation method comprises at least one fromamong transformation of rotation, symmetry, enlargement, reduction, andtranslation.
 5. The lithography model generating method of claim 2,wherein the process variation includes a variation of at least one fromamong a dose, a focus, and an anchor pattern.
 6. The lithography modelgenerating method of claim 1, wherein the deep learning is performed byapplying a weight to the basic image data and the transform image datawhile changing a combination of a number of pieces of the basic imagedata and a number of pieces of the transform image data used as inputdata for each iteration of the deep learning.
 7. The lithography modelgenerating method of claim 1, wherein the deep learning is performedusing deep convolutional generative adversarial networks (DCGAN).
 8. Thelithography model generating method of claim 1, further comprising:adjusting a recipe with respect to the lithography model, wherein theverifying of the lithography model comprises: proceeding to theadjusting of the recipe based on a set condition being satisfied, andproceeding to the generating of the lithography model based on the setcondition not being satisfied.
 9. The lithography model generatingmethod of claim 8, wherein the lithography model is an optical proximitycorrection (OPC) model, or a process proximity correction (PPC) model,and wherein the set condition is whether an error root mean square(RMS), of a comparison of image data output from the lithography modelwith reference image data, is less than a set value.
 10. The lithographymodel generating method of claim 1, wherein the basic image data is datawith respect to an after develop inspection (ADI) contour image.
 11. Alithography model generating method based on deep learning, thelithography model generating method comprising: preparing basic imagedata for learning; preparing transform image data that indicates a maskbias variation; generating a lithography model by performing deeplearning by combining the basic image data and the transform image data;verifying the lithography model; and adjusting a recipe with respect tothe lithography model, wherein the transform image data is generatedthrough an image augmentation method from the basic image data such thatthe transform image data indicates a process variation.
 12. Thelithography model generating method of claim 11, wherein the processvariation includes a variation of at least one from among a dose, afocus, and an anchor pattern.
 13. The lithography model generatingmethod of claim 11, wherein the deep learning is performed using deepconvolutional generative adversarial networks (DCGAN), and wherein thedeep learning is performed by applying a weight while changing acombination of a number of pieces of the basic image data and a numberof pieces of the transform image data used as input data for eachiteration of the deep learning.
 14. The lithography model generatingmethod of claim 11, wherein the verifying of the lithography modelcomprises: proceeding to the adjusting of the recipe based on a setcondition being satisfied, and proceeding to the generating of thelithography model based on the set condition not being satisfied. 15.The lithography model generating method of claim 11, wherein the imageaugmentation method comprises using at least one from amongtransformation of rotation, symmetry, enlargement, reduction, andtranslation.
 16. A mask manufacturing method comprising: generating alithography model based on deep learning; generating an opticalproximity correction (OPC)ed layout by performing OPC on a mask layoutby using an OPC model obtained based on the lithography model;transferring the OPCed layout as mask tape-out (MTO) design data;preparing mask data based on the MTO design data; and exposing asubstrate for a mask based on the mask data, wherein the generating ofthe lithography model comprises: preparing basic image data forlearning; preparing transform image data that indicates a mask biasvariation; generating the lithography model by performing deep learningby combining the basic image data and the transform image data;verifying the lithography model; and adjusting a recipe with respect tothe lithography model, and wherein the lithography model comprises anoptical proximity correction (OPC) model or a process proximitycorrection (PPC) model.
 17. The mask manufacturing method of claim 16,wherein the transform image data is generated from the basic image datasuch that the transform image data indicates a process variation throughan image augmentation method.
 18. The mask manufacturing method of claim17, wherein the process variation includes a variation of at least onefrom among a dose, a focus, and an anchor pattern.
 19. The maskmanufacturing method of claim 17, wherein the deep learning is performedusing deep convolutional generative adversarial networks (DCGAN), andwherein the deep learning is performed by applying a weight whilechanging a combination of a number of pieces of the basic image data anda number of pieces of the transform image data used as input data foreach iteration of the deep learning.
 20. The mask manufacturing methodof claim 17, wherein the verifying of the lithography model comprises:proceeding to the adjusting of the recipe based on a set condition beingsatisfied, and proceeding to the generating of the lithography modelbased on the set condition not being satisfied.